Transporting hydrocarbons from production sites to treating plants constitutes an important link in the petroleum chain. It is a delicate link because of the complex interactions between the phases forming the transported effluents. The basic objective for operators is to reach an optimum productivity under the best safety conditions. They therefore have to control as best they can the velocity and the temperature so as to avoid unnecessary pressure drops, unwanted deposits and unsteady-state flows. The method that is generally used consists in modelling in the best possible way the transportation of complex multiphase streams so as to provide at all times an image of the flows in the various parts of the production chain, taking into account the precise constitution of the effluent, the flow rates, the pressures and the flow regimes.
There are currently various software tools for simulating the transport of complex multiphase streams, allowing to design suitable production equipments at an early stage.
Patents U.S. Pat. No. 5,550,761, FR-2,756,044 (U.S. Pat. No. 6,028,992) and FR-2,756,045 (U.S. Pat. No. 5,960,187) filed by the applicant notably describe modelling methods and tools allowing to simulate the transport of complex multiphase streams on steady or transient flow and capable of taking into account instability phenomena that occur because of the irregular geometry of the formation crossed by the pipe or of the topography thereof, referred to by specialists as <<terrain slugging >> or <<severe slugging >>.
The simulation tools are as complex as the modelled phenomena. Precision and performances can only be obtained after a relatively long modelling time, which is not really compatible with real-time management.
Another approach allowing, alone or in parallel with the above modelling methods, real-time management of the parameters of a fluid circulation uses neural networks.
It can be reminded that neural networks define a data processing mode simulating the functioning of biological neural systems. In such networks, an element carries out a relatively simple calculation such as a weighted sum of the signals present at its inputs applied to a non-linear function, which determines the state of the output thereof. A large number of such elements, interconnected in series and in parallel, is used. Suitable selection of the weighting factors allows the network to carry out complex functions. Networks known as retropropagation networks for example use multiple layers of elements as defined above. Adaptation of such a network to a precise task is carried out by “training” the network on a certain number of examples and by adjusting the weighting factors for each element to the suitable values. Input values are presented to the network, the output value produced by the network is analysed and the weighting factors are modified so as to minimize in the best possible way the difference between the effective value at the output and the value expected in the example selected. After sufficient training, the network is suited to respond to new input values for which the output value is not known a priori and to produce a suitable output value. In its principle, a neural network works according to a non-linear regression method which is all the more effective in relation to conventional methods. Two network types can be used, mainly MLP (Multi Layer Perceptron) networks, or Kohonen networks, well-known to specialists.
Patent EP-1,176,481 filed by the applicant describes a method intended for real-time estimation of the flow regime, at any point of a pipe whose structure is defined by a certain number of structure and physical parameters, of a multiphase fluid stream defined by several physical quantities and comprising liquid and gas phases. According to this method, the flow regime is modelled by forming a non-linear neural network with an input layer having as many inputs as there are structure parameters and physical quantities, an output layer with as many outputs as there are quantities necessary for estimation of the flow regime, and at least one intermediate layer, by forming a learning base with predetermined tables connecting various values obtained for the output data to the corresponding values of the input data, and by determining, by iterations, weighting factors of the activation function allowing to properly connect the values in the input and output data tables.
Output data of the neurons is preferably analysed so as to sort, among the values of the output data of the neural network, only the pertinent data to be taken into account for iterative determination of the weighting coefficients of the activation function.
Patent EP-1,217,474 also filed by the applicant describes a method allowing to construct a module (hydrodynamic or thermodynamic for example) that it is best suited to fixed operating conditions depending on the structure of the pipe and on a set of determined physical quantities (hydrodynamic or thermodynamic quantities for example), with fixed variation ranges for the parameters and the physical quantities. The learning base is adapted to the imposed operating conditions and optimized neural networks best adjusted to the imposed operating conditions are generated. In the case, for example, where the module is to be integrated in a general multiphase flow simulation model, both hydrodynamic and thermodynamic, the model is used to form the learning base so as to select the set of physical quantities that is best suited to the operation of the model, as well as the variation ranges fixed for said parameters and said physical quantities, and the optimized neural networks that best adjust to the learning base formed are generated.
In the aforementioned prior methods, the flows are considered in a global way, without distinction between the various possible flow regimes of the fluids in the pipe: stratified flow, dispersed flow, intermittent flow, whose behaviours are different. This can lead to modelling errors that are too great in relation to the estimation quality required for production monitoring. Furthermore, they do not take account of the existence of simple models (for example analytic models) translating in mathematical form characteristics of one or more flow regimes.